Published on March 14, 2018 by Microsoft Research

Dense Associative Memories are generalizations of Hopfield nets to higher order (higher than quadratic) interactions between the spins/neurons. I will describe a relationship between these models and neural networks commonly used in deep learning. From the perspective of associative memory, such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. From the perspective of deep learning, these models make it possible to control the kind of representation that the neural networks learn from a given dataset: small powers of the interaction vertex correspond to feature-based representations, large powers – to prototypes. These Dense Associative Memories can be driven by images processed with convolutional neural networks generally used in image analysis. I will discuss the potential for using this idea to mitigate the problem of adversarial images (very small changes to an input image which lead to a gross misclassification) in computer vision.

See more at www.microsoft.com/en-us/research/video/dense-associative-memories-and-deep-learning/

Leave a Reply

4 Comments on "Dense Associative Memories and Deep Learning"

Notify of
avatar

Pragy Agarwal
Guest
Pragy Agarwal
3 months 8 days ago
Abhijit Mophare
Guest
Abhijit Mophare
3 months 8 days ago

great job

Don Beckham
Guest
Don Beckham
3 months 8 days ago
Does anyone read these comments? I have commented on many videos about the same problem. Do you realize just how difficult it it to follow the subjects when you throw the slide of for just a couple of seconds and then cut back to the speaker for 99% of the video? I am going to unsubscribe if you don't correct this soon. This is becoming a trend and it's really a shame that "Microsoft" would have such a poor presentation of bleeding edge information. But, such is life. I guess the old corporate saying is true, "people are promoted to… Read more »
krishna
Guest
krishna
3 months 9 days ago

Whats the point of focusing on the person instead of focusing on slides..

wpDiscuz